About this book

Biosecurity Surveillance: Quantitative Approaches is a source of information on practical and innovative approaches to biosecurity surveillance. lt explains the foundation and concepts behind surveillance design, with examples of methods and tools created to deal with surveillance challenges. With supporting case studies and including current directions in research, it covers evidence-based approaches to surveillance, statistics, detectability, single and multi-species detection, risk assessment, diagnostics, databasing, modelling of invasion and spread, optimisation, and future climate challenges.

Contents

1 Introduction to biosecurity surveillance2 Biosecurity surveillance in agriculture and environment: a review3 Getting the story straight: Laying the foundations for statistical evaluation of the performance of surveillance4 Hierarachial models for evaluating surveillance strategies: diversity within a common modular structure5 The relationship between biosecurity surveillance and risk assessment6 Designing surveillance for emergency response7 The role of surveillance in evaluating and comparing international quarantine systems8 Estimating Detection Rates & Probabilities9 Estimating non-compliance rate using imperfect and incomplete information10 Surveillance for microbial soilborne pathogens and biocontrol organisms11 Design of a surveillance system for non-indigenous species on Barrow Island: plants case study12 Towards reliable mapping of biosecurity risk: incorporating uncertainty and decision-makers’ risk aversion13 Detection survey design for decision making during biosecurity incursions14 Inference and prediction with individual-based stochastic models of epidemics15 Evidence of Absence for Invasive Species: Roles for Hierarchical Bayesian Approaches in Regulation16 Surveillance as part of an SPS system: using bayesian networks to better understand plant and animal surveillance17 Statistical emulators of simulation models to inform response to new biological invasions18 Animal, vegetable, or …? A case study in using animal-health monitoring design tools to solve a plant-health surveillance problem19 Agent-Based Bayesian Spread Model Applied to Red Imported Fire Ants in Brisbane

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